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Bioinformatics Advance Access published online on May 3, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn218
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

Bobbie-Jo M. Webb-Robertson 1,*, William R. Cannon 1, Christopher S. Oehmen 1, Anuj R. Shah 2, Vidhya Gurumoorthi 3, Mary S. Lipton 4 and Katrina M. Waters 1

1Computational Biology & Bioinformatics, Pacific Northwest National Laboratory
2Scientific Data Management, Pacific Northwest National Laboratory
3Applied Computer Science, Pacific Northwest National Laboratory
4Biological Separations and Mass Spectrometry, Pacific Northwest National Laboratory

*To whom correspondence should be addressed. Dr. Bobbie-Jo M. Webb-Robertson, E-mail: bj{at}pnl.gov


  Abstract

Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares these profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic).

Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage.

Availability: http://omics.pnl.gov/software/STEPP.php

Associate Editor: Prof. Quackenbush

Received on March 18, 2008; revised on April 18, 2008; accepted on April 29, 2008

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